Project Part 2

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This notebook contains the code samples found in Chapter 3, Section 5 of Deep Learning with R. Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.

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Data Exploration & Preparation

• Our goal in the second part of the assignment is to predict how good a (new) customer will pay back their credit card depts. In the data set application data from current customers (the first 18 attributes) together with their status (last attribute; target) are given.
  
• The attributes from the applications are

Attribute Name	Explanation	Remarks
ID	Client	number
CODE_GENDER	Gender
FLAG_OWN_CAR	Is there a car
FLAG_OWN_REALTY	Is there a property
CNT_CHILDREN	Number of children
AMT_INCOME_TOTAL	Annual income
NAME_INCOME_TYPE	Income category
NAME_EDUCATION_TYPE	Education level
NAME_FAMILY_STATUS	Marital status
NAME_HOUSING_TYPE	Way of living
DAYS_BIRTH	Birthday	Count backwards from current day (0), -1 means yesterday
DAYS_EMPLOYED	Start date of employment	Count backwards from current day(0). If positive, it means the person unemployed.
FLAG_MOBIL	Is there a mobile phone
FLAG_WORK_PHONE	Is there a work phone
FLAG_PHONE	Is there a phone
FLAG_EMAIL	Is there an email
OCCUPATION_TYPE	Occupation
CNT_FAM_MEMBERS	Family size

• The last attribute status contains the “pay-back behavior”, i.e. when did that customer pay back their depts:
  • 0: 1-29 days past due
  • 1: 30-59 days past due
  • 2: 60-89 days overdue
  • 3: 90-119 days overdue
  • 4: 120-149 days overdue
  • 5: Overdue or bad debts, write-offs for more than 150 days
  • C: paid off that month
  • X: No loan for the month Please note: We are learning only the pay-back behavior. The decision, i.e. if we accept a customer or not, is done in another process step – not here!

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Main task

• Design your network. Why did you use a feed-forward network, or a convolutional or recursive network – and why not?
  
• Use k-fold validation (with k = 10) to find the best hyperparameters for your network.
• Use the average of the accuracy to evaluate the performance of your trained network.
• Find a “reasonable” good model. Argue why that model is reasonable. If you are not able to find a reasonable good model, explain what you all did to find a good model and argue why you think that’s not a good model.
  
• Save your trained neural network with save_model_hdf5. Also save your data sets you used for training, testing and validation.

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Some hints

• Data preprocessing is easier here; no feature engineering is needed.
• You may be able to reuse parts of the exercises we used in our examples during lectures.
• All in- and output values need to be floating numbers (or integers in exceptions) in the range of [0,1].
• Please note that a neural network expects a R matrix or vector, not data frames. Transform your data (e.g. a data frame) into a matrix with data.matrix if needed.
  
• There are some models which show an accuracy higher than 90% (!) for training (and test) data – after learning more than 1000 epochs.

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Important notes

• Single-label, Multiclass classification problem on page 73 in Deep Learning with R
• Spaces must be removed in between ‘{r}' and '’, else an error with ‘’ will be produced
• K-Fold Validation on page 83ff and 94ff in Deep Learning with R
• Page 110, use Last-Layer activation softmax and loss function categorical_crossentropy
• Convolutional network ausgeschlossen, weil hauptsächlich Pattern recognition/image classification
• Recursive ausgeschlossen, weil hauptsächlich für TimeSeries-Vorhersagen verwendet, oder für Vorhersagen
• Feed-Forward, weil Classification-Task

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Data import

#install.packages("tidymodels")
#install.packages("themis")
library(here)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(tensorflow)
library(tfdatasets)
library(tidymodels)
library(keras)
library(caret)
library(themis)
#LOAD DATA
setwd(getwd())
dataIn = "../Data/Dataset-part-2.csv"
data_in <- read.csv(dataIn,header = TRUE, sep =',')
#View(data_in)
data <- data.frame(data_in)
summary(data)

       ID          CODE_GENDER        FLAG_OWN_CAR       FLAG_OWN_REALTY     CNT_CHILDREN     AMT_INCOME_TOTAL 
 Min.   :5008804   Length:67614       Length:67614       Length:67614       Min.   : 0.0000   Min.   :  26100  
 1st Qu.:5465941   Class :character   Class :character   Class :character   1st Qu.: 0.0000   1st Qu.: 112500  
 Median :5954270   Mode  :character   Mode  :character   Mode  :character   Median : 0.0000   Median : 157500  
 Mean   :5908133                                                            Mean   : 0.4206   Mean   : 178867  
 3rd Qu.:6289080                                                            3rd Qu.: 1.0000   3rd Qu.: 225000  
 Max.   :7965248                                                            Max.   :19.0000   Max.   :6750000  
 NAME_INCOME_TYPE   NAME_EDUCATION_TYPE NAME_FAMILY_STATUS NAME_HOUSING_TYPE    DAYS_BIRTH     DAYS_EMPLOYED   
 Length:67614       Length:67614        Length:67614       Length:67614       Min.   :-25201   Min.   :-17531  
 Class :character   Class :character    Class :character   Class :character   1st Qu.:-19438   1st Qu.: -2886  
 Mode  :character   Mode  :character    Mode  :character   Mode  :character   Median :-15592   Median : -1305  
                                                                              Mean   :-15914   Mean   : 62022  
                                                                              3rd Qu.:-12347   3rd Qu.:  -321  
                                                                              Max.   : -7489   Max.   :365243  
   FLAG_MOBIL FLAG_WORK_PHONE    FLAG_PHONE       FLAG_EMAIL     OCCUPATION_TYPE    CNT_FAM_MEMBERS 
 Min.   :1    Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Length:67614       Min.   : 1.000  
 1st Qu.:1    1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   Class :character   1st Qu.: 2.000  
 Median :1    Median :0.0000   Median :0.0000   Median :0.0000   Mode  :character   Median : 2.000  
 Mean   :1    Mean   :0.2028   Mean   :0.2742   Mean   :0.1005                      Mean   : 2.174  
 3rd Qu.:1    3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:0.0000                      3rd Qu.: 3.000  
 Max.   :1    Max.   :1.0000   Max.   :1.0000   Max.   :1.0000                      Max.   :20.000  
    status         
 Length:67614      
 Class :character  
 Mode  :character  
                   
                   
                   

plot(data$status)

##Cleanup

# Check for duplicates 
sum(duplicated(data))

[1] 0

# No duplicates

#Remove ID (irrelevant) and FLAG_MOBIL (always 1)
data <- data %>% select(-ID, -FLAG_MOBIL)
cols <- c("CODE_GENDER","FLAG_OWN_CAR","FLAG_OWN_REALTY","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE", "NAME_FAMILY_STATUS", "NAME_HOUSING_TYPE","FLAG_WORK_PHONE","FLAG_PHONE","FLAG_EMAIL", "OCCUPATION_TYPE","status")
cols

 [1] "CODE_GENDER"         "FLAG_OWN_CAR"        "FLAG_OWN_REALTY"     "NAME_INCOME_TYPE"   
 [5] "NAME_EDUCATION_TYPE" "NAME_FAMILY_STATUS"  "NAME_HOUSING_TYPE"   "FLAG_WORK_PHONE"    
 [9] "FLAG_PHONE"          "FLAG_EMAIL"          "OCCUPATION_TYPE"     "status"             

data[cols] <- lapply(data[cols],factor)

# Replacing empty values with "Unknown"
levels(data$OCCUPATION_TYPE) <- c(levels(data$OCCUPATION_TYPE), "Unknown")
data$OCCUPATION_TYPE[is.na(data$OCCUPATION_TYPE)] <- "Unknown"

# Replacing C and X in Status
levels(data$status)[levels(data$status)=="C"] <- "6"
#data$status[data$status == "X"] <- 7
levels(data$status)[levels(data$status)=="X"] <- "7"
# #Convert factors into numericals
# data %<>% mutate_if(is.factor, as.numeric)

summary(data)

 CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN     AMT_INCOME_TOTAL              NAME_INCOME_TYPE
 F:43924     N:43107      N:21090         Min.   : 0.0000   Min.   :  26100   Commercial associate:15640  
 M:23690     Y:24507      Y:46524         1st Qu.: 0.0000   1st Qu.: 112500   Pensioner           :11982  
                                          Median : 0.0000   Median : 157500   State servant       : 5044  
                                          Mean   : 0.4206   Mean   : 178867   Student             :    4  
                                          3rd Qu.: 1.0000   3rd Qu.: 225000   Working             :34944  
                                          Max.   :19.0000   Max.   :6750000                               
                                                                                                          
                    NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS           NAME_HOUSING_TYPE
 Academic degree              :   38    Civil marriage      : 6016    Co-op apartment    :  227  
 Higher education             :16890    Married             :44906    House / apartment  :60307  
 Incomplete higher            : 2306    Separated           : 4125    Municipal apartment: 2303  
 Lower secondary              :  716    Single / not married: 9528    Office apartment   :  587  
 Secondary / secondary special:47664    Widow               : 3039    Rented apartment   : 1020  
                                                                      With parents       : 3170  
                                                                                                 
   DAYS_BIRTH     DAYS_EMPLOYED    FLAG_WORK_PHONE FLAG_PHONE FLAG_EMAIL    OCCUPATION_TYPE  CNT_FAM_MEMBERS 
 Min.   :-25201   Min.   :-17531   0:53904         0:49071    0:60819    Unknown    :20699   Min.   : 1.000  
 1st Qu.:-19438   1st Qu.: -2886   1:13710         1:18543    1: 6795    Laborers   :12425   1st Qu.: 2.000  
 Median :-15592   Median : -1305                                         Sales staff: 6899   Median : 2.000  
 Mean   :-15914   Mean   : 62022                                         Core staff : 6059   Mean   : 2.174  
 3rd Qu.:-12347   3rd Qu.:  -321                                         Managers   : 4950   3rd Qu.: 3.000  
 Max.   : -7489   Max.   :365243                                         Drivers    : 4427   Max.   :20.000  
                                                                         (Other)    :12155                   
     status     
 0      :52133  
 1      : 6491  
 7      : 5790  
 6      : 1805  
 2      :  712  
 5      :  374  
 (Other):  309  

Preprocessing

set.seed(1)
trainIndex <- initial_split(data, prop = 0.7, strata = status) 
trainingSet <- training(trainIndex)
testSet <- testing(trainIndex)
status_folds <- vfold_cv(trainingSet, v = 10, strata = status)
status_folds

#  10-fold cross-validation using stratification 

set.seed(5)
preprocRecipe <-
  recipe(status ~., data = data) %>%
  step_dummy(all_nominal(), -status,  one_hot = TRUE) %>%
  step_range(all_predictors(), -all_nominal(), min = 0, max = 1)%>%
 # step_downsample(status, over_ratio = 1) %>%
  step_smote(status, over_ratio = 0.5, skip=TRUE) %>%
 # step_smotenc(status, over_ratio = 1) %>%
 #step_adasyn(status, over_ratio = 1) %>%
 #step_nearmiss(status, over_ratio = 1) %>%
   
  step_dummy(status,  one_hot = TRUE)# %>%

In this step the above defined receipt is extracted using the prep() function, and then use the bake() function to transform a set of data based on that recipe.

# retain = TRUE and new_data = NULL ensures that pre-processed trainingSet is returned 
trainingSet_processed <- preprocRecipe %>%
  prep(trainingSet, retain = TRUE) %>%
  bake(new_data = NULL)
testSet_processed <- preprocRecipe %>%
  prep(testSet) %>%
  bake(new_data =testSet)

#summary(trainingSet_processed)

#OneHotEncoding

# # One-Hot-Encoding for CODE_GENDER
# dmy <- dummyVars(" ~ CODE_GENDER", data = data)
# trsf <- data.frame(predict(dmy, newdata = data)) 
# data <- cbind(data, trsf)
# data <- data %>% select(-CODE_GENDER)
# 
# # One-Hot-Encoding for FLAG_OWN_CAR 
# dmyCar <- dummyVars(" ~ FLAG_OWN_CAR", data = data)
# trsfCar <- data.frame(predict(dmyCar, newdata = data))
# data <- cbind(data, trsfCar)
# data <- data %>% select(-FLAG_OWN_CAR)
# 
# # One-Hot-Encoding for FLAG_OWN_REALTY   
# dmyRealty <- dummyVars(" ~ FLAG_OWN_REALTY", data = data)
# trsfRealty <- data.frame(predict(dmyRealty, newdata = data))
# data <- cbind(data, trsfRealty)
# data <- data %>% select(-FLAG_OWN_REALTY)
# 
# # One-Hot-Encoding for NAME_INCOME_TYPE   
# dmyNIT <- dummyVars(" ~ NAME_INCOME_TYPE", data = data)
# trsfNIT <- data.frame(predict(dmyNIT, newdata = data))
# data <- cbind(data, trsfNIT)
# data <- data %>% select(-NAME_INCOME_TYPE)
# 
# # Factoring NAME_EDUCATION_TYPE as it is ordinal
# # unique(data$NAME_EDUCATION_TYPE)
# # "Secondary / secondary special", "Higher education", "Incomplete higher", "Lower secondary", "Academic degree"
# # Ranking:
# # 'Lower secondary', 'Secondary / secondary special', 'Incomplete higher', 'Higher education', 'Academic degree'
# data$NAME_EDUCATION_TYPE <- as.numeric(factor(data$NAME_EDUCATION_TYPE, order=TRUE, levels=c('Lower secondary', 'Secondary / secondary special', 'Incomplete higher', 'Higher education', 'Academic degree')))
# #dmyNET <- dummyVars(" ~ NAME_EDUCATION_TYPE", data = data)
# #trsfNET <- data.frame(predict(dmyNET, newdata = data))
# #data <- cbind(data, trsfNET)
# #data <- data %>% select(-NAME_EDUCATION_TYPE)
# 
# # One-Hot-Encoding for NAME_FAMILY_STATUS   
# dmyNFS <- dummyVars(" ~ NAME_FAMILY_STATUS", data = data)
# trsfNFS <- data.frame(predict(dmyNFS, newdata = data))
# data <- cbind(data, trsfNFS)
# data <- data %>% select(-NAME_FAMILY_STATUS)
# 
# # One-Hot-Encoding for NAME_HOUSING_TYPE   
# dmyNHT <- dummyVars(" ~ NAME_HOUSING_TYPE", data = data)
# trsfNHT <- data.frame(predict(dmyNHT, newdata = data))
# data <- cbind(data, trsfNHT)
# data <- data %>% select(-NAME_HOUSING_TYPE)
# 
# # Remove FLAG_MOBIL, it is always 1
# #data <- data %>% select(-FLAG_MOBIL)
# 
# # One-Hot-Encoding for FLAG_WORK_PHONE  
# # Not needed, already 1 or 0
# # dmyFWP <- dummyVars(" ~ FLAG_WORK_PHONE", data = data)
# # trsfFWP <- data.frame(predict(dmyFWP, newdata = data))
# # data <- cbind(data, trsfFWP)
# # data <- data %>% select(-FLAG_WORK_PHONE)
# 
# # One-Hot-Encoding for FLAG_PHONE  
# # Not needed, already 1 or 0
# # dmyFP <- dummyVars(" ~ FLAG_PHONE", data = data)
# # trsfFP <- data.frame(predict(dmyFP, newdata = data))
# # data <- cbind(data, trsfFP)
# # data <- data %>% select(-FLAG_PHONE)
# 
# # One-Hot-Encoding for FLAG_EMAIL 
# # Not needed, already 1 or 0
# # dmyFE <- dummyVars(" ~ FLAG_EMAIL", data = data)
# # trsfFE <- data.frame(predict(dmyFE, newdata = data))
# # data <- cbind(data, trsfFE)
# # data <- data %>% select(-FLAG_EMAIL)
# 
# # One-Hot-Encoding for OCCUPATION_TYPE
# #data$OCCUPATION_TYPE
# #levels(data$OCCUPATION_TYPE)
# levels(data$OCCUPATION_TYPE) <- c(levels(data$OCCUPATION_TYPE), "Unknown")
# data$OCCUPATION_TYPE[is.na(data$OCCUPATION_TYPE)] <- "Unknown"
# dmyOT <- dummyVars(" ~ OCCUPATION_TYPE ", data = data)
# trsfOT <- data.frame(predict(dmyOT, newdata = data))
# data <- cbind(data, trsfOT)
# data <- data %>% select(-OCCUPATION_TYPE )
# 
# #summary(data)
# 
# ### Normalizing data
# 
# ## Does not work for single columns
# # preprocessParams <- preProcess(data$CNT_CHILDREN, method=c("center", "scale"))
# # summarize transform parameters
# # print(preprocessParams)
# # transform the dataset using the parameters
# # transformed <- predict(preprocessParams, iris[,1:4])
# 
# ## Does not work for single columns
# # mean <- apply(data, 2, mean)
# # std <- apply(data, 2, sd)
# # dataScaled <- scale(data$CNT_CHILDREN, center = mean, scale = std)
# 
# # ## Based on https://www.learnbymarketing.com/tutorials/neural-networks-in-r-tutorial/
# # #Might not be needed if done in preprocessing 
# # data$CNT_CHILDREN <- (data$CNT_CHILDREN-min(data$CNT_CHILDREN)) / (max(data$CNT_CHILDREN)-min(data$CNT_CHILDREN))
# # data$DAYS_BIRTH <- (data$DAYS_BIRTH-min(data$DAYS_BIRTH)) / (max(data$DAYS_BIRTH)-min(data$DAYS_BIRTH))
# # data$DAYS_EMPLOYED <- (data$DAYS_EMPLOYED-min(data$DAYS_EMPLOYED)) / (max(data$DAYS_EMPLOYED)-min(data$DAYS_EMPLOYED))
# # data$CNT_FAM_MEMBERS <- (data$CNT_FAM_MEMBERS-min(data$CNT_FAM_MEMBERS)) / (max(data$CNT_FAM_MEMBERS)-min(data$CNT_FAM_MEMBERS))
# 
# # ggplot(data = data, mapping = aes(x = ID, y = AMT_INCOME_TOTAL)) + geom_point()
# # heavily right skewed, but NN should not be affected by it
# #data$AMT_INCOME_TOTAL <- (data$AMT_INCOME_TOTAL-min(data$AMT_INCOME_TOTAL)) / (max(data$AMT_INCOME_TOTAL)-min(data$AMT_INCOME_TOTAL))
# 
# summary(data)

Check data

# dim(data)
# sapply(data, class)
# levels(data$status)
# 
# #Replace C and X in status
# #data$status[data$status == "C"] <- 6
# levels(data$status)[levels(data$status)=="C"] <- "6"
# #data$status[data$status == "X"] <- 7
# levels(data$status)[levels(data$status)=="X"] <- "7"
# #Convert factors into numericals
# data %<>% mutate_if(is.factor, as.numeric)
# levels(data$status)
# sapply(data, class)

# summarize the class distribution
percentage <- prop.table(table(data$status)) * 100
cbind(freq=table(data$status), percentage=percentage)

   freq percentage
0 52133 77.1038542
1  6491  9.6000828
2   712  1.0530364
3   195  0.2884018
4   114  0.1686041
5   374  0.5531399
6  1805  2.6695655
7  5790  8.5633153

# Turn data frame into data matrix
matrix_data <- trainingSet_processed %>% select(-tail(names(trainingSet_processed), 8))
#matrix_data <- subset(data, select = c(CNT_CHILDREN, AMT_INCOME_TOTAL))
#matrix_targets <- data.matrix(trainingSet_processed[])
matrix_targets <- trainingSet_processed %>% select(tail(names(trainingSet_processed), 8))

matrix_data_test  <- testSet_processed %>% select(-tail(names(testSet_processed), 8))
matrix_targets_test  <- testSet_processed %>% select(tail(names(testSet_processed), 8))

#Subset only 100 entries for testing
#matrix_data <- matrix_data[1:100, ]
#matrix_targets <- matrix_targets[1:100, ]

Build Model

#train_data <- matrix_data
train_data <- data.matrix(matrix_data)
test_data <- data.matrix(matrix_data_test)
train_targets <- data.matrix(matrix_targets)
test_targets <- data.matrix(matrix_targets_test)

# Function to build the model
build_model <- function() {
  model <- keras_model_sequential() %>%
    #layer_batch_normalization(axis = -1L, input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 64, activation = "relu", input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 128, activation = "relu") %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 8, activation = "softmax") 

  model %>% compile(
    optimizer = optimizer_sgd(learning_rate = 0.2),
    loss = "categorical_crossentropy",
    metrics = "categorical_accuracy"
  )

}

K-Fold-Validation

# mean <- apply(matrix_data, 2, mean)
# std <- apply(matrix_data, 2, sd)
# train_data <- scale(matrix_data, center = mean, scale = std)
# test_data <- scale(matrix_data, center = mean, scale = std)
# train_targets <- matrix_targets


k <- 10
indices <- sample(1:nrow(train_data))
folds <- cut(indices, breaks = k, labels = FALSE)

num_epochs <- 10
all_acc_histories <- NULL
for (i in 1:k) {
  cat("processing fold #", i, "\n")

  val_indices <- which(folds == i, arr.ind = TRUE)
  val_data <- train_data[val_indices,] #test_data#
  val_targets <- train_targets[val_indices,] #test_targets#
  
  partial_train_data <- train_data[-val_indices,]
  partial_train_targets <- train_targets[-val_indices,]
  model <- build_model()

  # Train the model (in silent mode, verbose=0)
  # Batch size https://stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network
  # One epoch = one forward pass and one backward pass of all the training examples
  # Batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
  # Number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).
  # Batch size 32 much faster than 1, also the smaller the batch the less accurate the estimate of the gradient will be.
  history <- model %>% fit(
    partial_train_data, partial_train_targets,
    validation_data = list(val_data, val_targets),
    epochs = num_epochs, batch_size = 512, verbose = 1
  )
  acc_history <- history$metrics$val_categorical_accuracy
  all_acc_histories <- rbind(all_acc_histories, acc_history)
}

processing fold # 1 

2022-12-28 20:04:43.360839: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-28 20:04:44.084274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4617 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:2d:00.0, compute capability: 6.1

Epoch 1/10

  1/289 [..............................] - ETA: 4:06 - loss: 2.0837 - categorical_accuracy: 0.1172
 19/289 [>.............................] - ETA: 0s - loss: 2.0270 - categorical_accuracy: 0.2047  
 39/289 [===>..........................] - ETA: 0s - loss: 1.9941 - categorical_accuracy: 0.2255
 58/289 [=====>........................] - ETA: 0s - loss: 1.9653 - categorical_accuracy: 0.2479
 77/289 [======>.......................] - ETA: 0s - loss: 1.9338 - categorical_accuracy: 0.2689
 94/289 [========>.....................] - ETA: 0s - loss: 1.9046 - categorical_accuracy: 0.2851
111/289 [==========>...................] - ETA: 0s - loss: 1.8748 - categorical_accuracy: 0.3004
130/289 [============>.................] - ETA: 0s - loss: 1.8487 - categorical_accuracy: 0.3116
150/289 [==============>...............] - ETA: 0s - loss: 1.8123 - categorical_accuracy: 0.3272
167/289 [================>.............] - ETA: 0s - loss: 1.7885 - categorical_accuracy: 0.3366
183/289 [=================>............] - ETA: 0s - loss: 1.7650 - categorical_accuracy: 0.3454
200/289 [===================>..........] - ETA: 0s - loss: 1.7417 - categorical_accuracy: 0.3537
214/289 [=====================>........] - ETA: 0s - loss: 1.7238 - categorical_accuracy: 0.3601
232/289 [=======================>......] - ETA: 0s - loss: 1.7009 - categorical_accuracy: 0.3684
252/289 [=========================>....] - ETA: 0s - loss: 1.6793 - categorical_accuracy: 0.3762
271/289 [===========================>..] - ETA: 0s - loss: 1.6565 - categorical_accuracy: 0.3844
289/289 [==============================] - 2s 3ms/step - loss: 1.6378 - categorical_accuracy: 0.3911

289/289 [==============================] - 3s 7ms/step - loss: 1.6378 - categorical_accuracy: 0.3911 - val_loss: 1.3113 - val_categorical_accuracy: 0.4992
Epoch 2/10

  1/289 [..............................] - ETA: 0s - loss: 1.3230 - categorical_accuracy: 0.5039
 19/289 [>.............................] - ETA: 0s - loss: 1.3482 - categorical_accuracy: 0.4979
 37/289 [==>...........................] - ETA: 0s - loss: 1.3391 - categorical_accuracy: 0.5029
 54/289 [====>.........................] - ETA: 0s - loss: 1.3275 - categorical_accuracy: 0.5051
 71/289 [======>.......................] - ETA: 0s - loss: 1.3165 - categorical_accuracy: 0.5070
 89/289 [========>.....................] - ETA: 0s - loss: 1.3007 - categorical_accuracy: 0.5124
106/289 [==========>...................] - ETA: 0s - loss: 1.2914 - categorical_accuracy: 0.5150
123/289 [===========>..................] - ETA: 0s - loss: 1.2820 - categorical_accuracy: 0.5186
139/289 [=============>................] - ETA: 0s - loss: 1.2743 - categorical_accuracy: 0.5212
156/289 [===============>..............] - ETA: 0s - loss: 1.2679 - categorical_accuracy: 0.5236
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289/289 [==============================] - 1s 3ms/step - loss: 1.2054 - categorical_accuracy: 0.5452

289/289 [==============================] - 1s 4ms/step - loss: 1.2054 - categorical_accuracy: 0.5452 - val_loss: 1.2781 - val_categorical_accuracy: 0.5161
Epoch 3/10

  1/289 [..............................] - ETA: 0s - loss: 1.1862 - categorical_accuracy: 0.5566
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289/289 [==============================] - 1s 3ms/step - loss: 1.0213 - categorical_accuracy: 0.6143

289/289 [==============================] - 1s 4ms/step - loss: 1.0213 - categorical_accuracy: 0.6143 - val_loss: 0.8964 - val_categorical_accuracy: 0.6582
Epoch 4/10

  1/289 [..............................] - ETA: 1s - loss: 0.8462 - categorical_accuracy: 0.6816
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289/289 [==============================] - 1s 3ms/step - loss: 0.9001 - categorical_accuracy: 0.6589

289/289 [==============================] - 1s 4ms/step - loss: 0.9001 - categorical_accuracy: 0.6589 - val_loss: 0.8783 - val_categorical_accuracy: 0.6634
Epoch 5/10

  1/289 [..............................] - ETA: 0s - loss: 0.8519 - categorical_accuracy: 0.6816
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289/289 [==============================] - 1s 3ms/step - loss: 0.8236 - categorical_accuracy: 0.6869

289/289 [==============================] - 1s 4ms/step - loss: 0.8236 - categorical_accuracy: 0.6869 - val_loss: 0.9088 - val_categorical_accuracy: 0.6484
Epoch 6/10

  1/289 [..............................] - ETA: 0s - loss: 0.8737 - categorical_accuracy: 0.6484
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289/289 [==============================] - 1s 3ms/step - loss: 0.7659 - categorical_accuracy: 0.7091

289/289 [==============================] - 1s 4ms/step - loss: 0.7659 - categorical_accuracy: 0.7091 - val_loss: 0.7209 - val_categorical_accuracy: 0.7199
Epoch 7/10

  1/289 [..............................] - ETA: 0s - loss: 0.6978 - categorical_accuracy: 0.7324
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289/289 [==============================] - 1s 3ms/step - loss: 0.7117 - categorical_accuracy: 0.7300

289/289 [==============================] - 1s 4ms/step - loss: 0.7117 - categorical_accuracy: 0.7300 - val_loss: 0.6908 - val_categorical_accuracy: 0.7377
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.7000 - categorical_accuracy: 0.7266
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289/289 [==============================] - 1s 4ms/step - loss: 0.6779 - categorical_accuracy: 0.7408

289/289 [==============================] - 1s 4ms/step - loss: 0.6779 - categorical_accuracy: 0.7408 - val_loss: 0.6726 - val_categorical_accuracy: 0.7436
Epoch 9/10

  1/289 [..............................] - ETA: 0s - loss: 0.6809 - categorical_accuracy: 0.7520
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289/289 [==============================] - 1s 4ms/step - loss: 0.6458 - categorical_accuracy: 0.7559

289/289 [==============================] - 1s 5ms/step - loss: 0.6458 - categorical_accuracy: 0.7559 - val_loss: 0.6179 - val_categorical_accuracy: 0.7673
Epoch 10/10

  1/289 [..............................] - ETA: 0s - loss: 0.6155 - categorical_accuracy: 0.7773
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289/289 [==============================] - 1s 4ms/step - loss: 0.6269 - categorical_accuracy: 0.7646

289/289 [==============================] - 1s 5ms/step - loss: 0.6269 - categorical_accuracy: 0.7646 - val_loss: 0.6031 - val_categorical_accuracy: 0.7744
processing fold # 2 
Epoch 1/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.6327 - categorical_accuracy: 0.3936

289/289 [==============================] - 2s 6ms/step - loss: 1.6327 - categorical_accuracy: 0.3936 - val_loss: 1.3443 - val_categorical_accuracy: 0.4929
Epoch 2/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.2094 - categorical_accuracy: 0.5468

289/289 [==============================] - 1s 5ms/step - loss: 1.2094 - categorical_accuracy: 0.5468 - val_loss: 1.1439 - val_categorical_accuracy: 0.5701
Epoch 3/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.0301 - categorical_accuracy: 0.6126

289/289 [==============================] - 1s 5ms/step - loss: 1.0301 - categorical_accuracy: 0.6126 - val_loss: 0.9806 - val_categorical_accuracy: 0.6175
Epoch 4/10

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289/289 [==============================] - 1s 4ms/step - loss: 0.9238 - categorical_accuracy: 0.6528

289/289 [==============================] - 1s 5ms/step - loss: 0.9238 - categorical_accuracy: 0.6528 - val_loss: 0.8937 - val_categorical_accuracy: 0.6543
Epoch 5/10

  1/289 [..............................] - ETA: 1s - loss: 0.8516 - categorical_accuracy: 0.6816
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289/289 [==============================] - 1s 4ms/step - loss: 0.8272 - categorical_accuracy: 0.6866

289/289 [==============================] - 1s 5ms/step - loss: 0.8272 - categorical_accuracy: 0.6866 - val_loss: 0.7765 - val_categorical_accuracy: 0.6993
Epoch 6/10

  1/289 [..............................] - ETA: 0s - loss: 0.7693 - categorical_accuracy: 0.7090
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289/289 [==============================] - 1s 4ms/step - loss: 0.7680 - categorical_accuracy: 0.7072

289/289 [==============================] - 1s 5ms/step - loss: 0.7680 - categorical_accuracy: 0.7072 - val_loss: 0.7760 - val_categorical_accuracy: 0.7022
Epoch 7/10

  1/289 [..............................] - ETA: 1s - loss: 0.7973 - categorical_accuracy: 0.6934
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289/289 [==============================] - 1s 4ms/step - loss: 0.7191 - categorical_accuracy: 0.7259

289/289 [==============================] - 1s 5ms/step - loss: 0.7191 - categorical_accuracy: 0.7259 - val_loss: 0.7119 - val_categorical_accuracy: 0.7253
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.6779 - categorical_accuracy: 0.7266
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289/289 [==============================] - 1s 4ms/step - loss: 0.6785 - categorical_accuracy: 0.7424

289/289 [==============================] - 1s 5ms/step - loss: 0.6785 - categorical_accuracy: 0.7424 - val_loss: 0.7016 - val_categorical_accuracy: 0.7237
Epoch 9/10

  1/289 [..............................] - ETA: 0s - loss: 0.6874 - categorical_accuracy: 0.7207
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289/289 [==============================] - 1s 4ms/step - loss: 0.6421 - categorical_accuracy: 0.7562

289/289 [==============================] - 1s 5ms/step - loss: 0.6421 - categorical_accuracy: 0.7562 - val_loss: 0.6234 - val_categorical_accuracy: 0.7617
Epoch 10/10

  1/289 [..............................] - ETA: 2s - loss: 0.6417 - categorical_accuracy: 0.7441
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289/289 [==============================] - 1s 4ms/step - loss: 0.6196 - categorical_accuracy: 0.7652

289/289 [==============================] - 1s 5ms/step - loss: 0.6196 - categorical_accuracy: 0.7652 - val_loss: 0.5963 - val_categorical_accuracy: 0.7677
processing fold # 3 
Epoch 1/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.6366 - categorical_accuracy: 0.3910

289/289 [==============================] - 2s 7ms/step - loss: 1.6366 - categorical_accuracy: 0.3910 - val_loss: 1.3589 - val_categorical_accuracy: 0.4777
Epoch 2/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.2230 - categorical_accuracy: 0.5423

289/289 [==============================] - 1s 5ms/step - loss: 1.2230 - categorical_accuracy: 0.5423 - val_loss: 1.0868 - val_categorical_accuracy: 0.6005
Epoch 3/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.0274 - categorical_accuracy: 0.6157

289/289 [==============================] - 1s 5ms/step - loss: 1.0274 - categorical_accuracy: 0.6157 - val_loss: 1.1429 - val_categorical_accuracy: 0.5624
Epoch 4/10

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289/289 [==============================] - 1s 4ms/step - loss: 0.9150 - categorical_accuracy: 0.6559

289/289 [==============================] - 1s 5ms/step - loss: 0.9150 - categorical_accuracy: 0.6559 - val_loss: 0.8330 - val_categorical_accuracy: 0.6860
Epoch 5/10

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289/289 [==============================] - 1s 4ms/step - loss: 0.8263 - categorical_accuracy: 0.6872

289/289 [==============================] - 1s 5ms/step - loss: 0.8263 - categorical_accuracy: 0.6872 - val_loss: 0.7603 - val_categorical_accuracy: 0.7077
Epoch 6/10

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289/289 [==============================] - 1s 4ms/step - loss: 0.7649 - categorical_accuracy: 0.7092

289/289 [==============================] - 1s 5ms/step - loss: 0.7649 - categorical_accuracy: 0.7092 - val_loss: 0.7160 - val_categorical_accuracy: 0.7293
Epoch 7/10

  1/289 [..............................] - ETA: 0s - loss: 0.7012 - categorical_accuracy: 0.7207
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289/289 [==============================] - 1s 4ms/step - loss: 0.7579 - categorical_accuracy: 0.7201

289/289 [==============================] - 1s 5ms/step - loss: 0.7579 - categorical_accuracy: 0.7201 - val_loss: 0.7756 - val_categorical_accuracy: 0.7096
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.7722 - categorical_accuracy: 0.7168
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289/289 [==============================] - 1s 4ms/step - loss: 0.6810 - categorical_accuracy: 0.7420

289/289 [==============================] - 1s 5ms/step - loss: 0.6810 - categorical_accuracy: 0.7420 - val_loss: 0.6368 - val_categorical_accuracy: 0.7593
Epoch 9/10

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289/289 [==============================] - 1s 4ms/step - loss: 0.6464 - categorical_accuracy: 0.7563

289/289 [==============================] - 1s 5ms/step - loss: 0.6464 - categorical_accuracy: 0.7563 - val_loss: 0.7373 - val_categorical_accuracy: 0.7235
Epoch 10/10

  1/289 [..............................] - ETA: 0s - loss: 0.7538 - categorical_accuracy: 0.7168
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289/289 [==============================] - 1s 4ms/step - loss: 0.6108 - categorical_accuracy: 0.7699

289/289 [==============================] - 1s 5ms/step - loss: 0.6108 - categorical_accuracy: 0.7699 - val_loss: 0.6235 - val_categorical_accuracy: 0.7563
processing fold # 4 
Epoch 1/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.6565 - categorical_accuracy: 0.3895

289/289 [==============================] - 2s 6ms/step - loss: 1.6565 - categorical_accuracy: 0.3895 - val_loss: 1.3926 - val_categorical_accuracy: 0.4753
Epoch 2/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.2066 - categorical_accuracy: 0.5471

289/289 [==============================] - 1s 5ms/step - loss: 1.2066 - categorical_accuracy: 0.5471 - val_loss: 1.1291 - val_categorical_accuracy: 0.5637
Epoch 3/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.0229 - categorical_accuracy: 0.6148

289/289 [==============================] - 1s 5ms/step - loss: 1.0229 - categorical_accuracy: 0.6148 - val_loss: 0.9193 - val_categorical_accuracy: 0.6590
Epoch 4/10

  1/289 [..............................] - ETA: 0s - loss: 0.8436 - categorical_accuracy: 0.6797
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289/289 [==============================] - 1s 4ms/step - loss: 0.8997 - categorical_accuracy: 0.6573

289/289 [==============================] - 1s 5ms/step - loss: 0.8997 - categorical_accuracy: 0.6573 - val_loss: 0.8795 - val_categorical_accuracy: 0.6655
Epoch 5/10

  1/289 [..............................] - ETA: 0s - loss: 0.8741 - categorical_accuracy: 0.6523
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289/289 [==============================] - 1s 4ms/step - loss: 0.8171 - categorical_accuracy: 0.6894

289/289 [==============================] - 1s 5ms/step - loss: 0.8171 - categorical_accuracy: 0.6894 - val_loss: 0.8894 - val_categorical_accuracy: 0.6707
Epoch 6/10

  1/289 [..............................] - ETA: 1s - loss: 0.8594 - categorical_accuracy: 0.6816
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289/289 [==============================] - 1s 4ms/step - loss: 0.7703 - categorical_accuracy: 0.7074

289/289 [==============================] - 1s 5ms/step - loss: 0.7703 - categorical_accuracy: 0.7074 - val_loss: 0.7090 - val_categorical_accuracy: 0.7332
Epoch 7/10

  1/289 [..............................] - ETA: 0s - loss: 0.6828 - categorical_accuracy: 0.7402
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289/289 [==============================] - 1s 4ms/step - loss: 0.7140 - categorical_accuracy: 0.7277

289/289 [==============================] - 1s 5ms/step - loss: 0.7140 - categorical_accuracy: 0.7277 - val_loss: 0.6668 - val_categorical_accuracy: 0.7506
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.6863 - categorical_accuracy: 0.7266
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289/289 [==============================] - 1s 4ms/step - loss: 0.6742 - categorical_accuracy: 0.7425

289/289 [==============================] - 1s 5ms/step - loss: 0.6742 - categorical_accuracy: 0.7425 - val_loss: 0.7039 - val_categorical_accuracy: 0.7390
Epoch 9/10

  1/289 [..............................] - ETA: 2s - loss: 0.6928 - categorical_accuracy: 0.7422
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289/289 [==============================] - 1s 4ms/step - loss: 0.6553 - categorical_accuracy: 0.7519

289/289 [==============================] - 1s 5ms/step - loss: 0.6553 - categorical_accuracy: 0.7519 - val_loss: 0.6029 - val_categorical_accuracy: 0.7727
Epoch 10/10

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289/289 [==============================] - 1s 4ms/step - loss: 0.6135 - categorical_accuracy: 0.7664

289/289 [==============================] - 1s 5ms/step - loss: 0.6135 - categorical_accuracy: 0.7664 - val_loss: 0.5717 - val_categorical_accuracy: 0.7851
processing fold # 5 
Epoch 1/10

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289/289 [==============================] - 1s 4ms/step - loss: 1.6412 - categorical_accuracy: 0.3896

289/289 [==============================] - 2s 6ms/step - loss: 1.6412 - categorical_accuracy: 0.3896 - val_loss: 1.6275 - val_categorical_accuracy: 0.4071
Epoch 2/10

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289/289 [==============================] - 2s 7ms/step - loss: 1.2163 - categorical_accuracy: 0.5402

289/289 [==============================] - 2s 8ms/step - loss: 1.2163 - categorical_accuracy: 0.5402 - val_loss: 1.1133 - val_categorical_accuracy: 0.5811
Epoch 3/10

  1/289 [..............................] - ETA: 0s - loss: 1.0462 - categorical_accuracy: 0.5898
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289/289 [==============================] - 2s 6ms/step - loss: 1.0259 - categorical_accuracy: 0.6110

289/289 [==============================] - 2s 7ms/step - loss: 1.0259 - categorical_accuracy: 0.6110 - val_loss: 0.9402 - val_categorical_accuracy: 0.6409
Epoch 4/10

  1/289 [..............................] - ETA: 2s - loss: 1.0395 - categorical_accuracy: 0.6250
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289/289 [==============================] - 1s 3ms/step - loss: 0.9151 - categorical_accuracy: 0.6515

289/289 [==============================] - 1s 4ms/step - loss: 0.9151 - categorical_accuracy: 0.6515 - val_loss: 0.8530 - val_categorical_accuracy: 0.6766
Epoch 5/10

  1/289 [..............................] - ETA: 0s - loss: 0.8958 - categorical_accuracy: 0.6543
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289/289 [==============================] - 1s 3ms/step - loss: 0.8438 - categorical_accuracy: 0.6791

289/289 [==============================] - 1s 4ms/step - loss: 0.8438 - categorical_accuracy: 0.6791 - val_loss: 0.8634 - val_categorical_accuracy: 0.6640
Epoch 6/10

  1/289 [..............................] - ETA: 0s - loss: 0.8177 - categorical_accuracy: 0.6777
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289/289 [==============================] - 1s 3ms/step - loss: 0.7766 - categorical_accuracy: 0.7031

289/289 [==============================] - 1s 4ms/step - loss: 0.7766 - categorical_accuracy: 0.7031 - val_loss: 0.7060 - val_categorical_accuracy: 0.7320
Epoch 7/10

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289/289 [==============================] - 1s 3ms/step - loss: 0.7230 - categorical_accuracy: 0.7239

289/289 [==============================] - 1s 4ms/step - loss: 0.7230 - categorical_accuracy: 0.7239 - val_loss: 0.7154 - val_categorical_accuracy: 0.7237
Epoch 8/10

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289/289 [==============================] - 1s 3ms/step - loss: 0.6872 - categorical_accuracy: 0.7387

289/289 [==============================] - 1s 4ms/step - loss: 0.6872 - categorical_accuracy: 0.7387 - val_loss: 0.6910 - val_categorical_accuracy: 0.7398
Epoch 9/10

  1/289 [..............................] - ETA: 0s - loss: 0.6499 - categorical_accuracy: 0.7793
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289/289 [==============================] - 1s 3ms/step - loss: 0.6523 - categorical_accuracy: 0.7520

289/289 [==============================] - 1s 3ms/step - loss: 0.6523 - categorical_accuracy: 0.7520 - val_loss: 0.6224 - val_categorical_accuracy: 0.7598
Epoch 10/10

  1/289 [..............................] - ETA: 0s - loss: 0.5660 - categorical_accuracy: 0.7852
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289/289 [==============================] - 1s 3ms/step - loss: 0.6295 - categorical_accuracy: 0.7626

289/289 [==============================] - 1s 3ms/step - loss: 0.6295 - categorical_accuracy: 0.7626 - val_loss: 0.6327 - val_categorical_accuracy: 0.7523
processing fold # 6 
Epoch 1/10

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289/289 [==============================] - 1s 3ms/step - loss: 1.6609 - categorical_accuracy: 0.3807

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Epoch 2/10

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289/289 [==============================] - 1s 3ms/step - loss: 1.2154 - categorical_accuracy: 0.5420

289/289 [==============================] - 1s 4ms/step - loss: 1.2154 - categorical_accuracy: 0.5420 - val_loss: 1.0517 - val_categorical_accuracy: 0.6042
Epoch 3/10

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289/289 [==============================] - 17s 57ms/step - loss: 1.0219 - categorical_accuracy: 0.6138

289/289 [==============================] - 18s 63ms/step - loss: 1.0219 - categorical_accuracy: 0.6138 - val_loss: 0.9336 - val_categorical_accuracy: 0.6522
Epoch 4/10

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289/289 [==============================] - 22s 77ms/step - loss: 0.9164 - categorical_accuracy: 0.6551

289/289 [==============================] - 24s 83ms/step - loss: 0.9164 - categorical_accuracy: 0.6551 - val_loss: 0.8339 - val_categorical_accuracy: 0.6881
Epoch 5/10

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289/289 [==============================] - 26s 91ms/step - loss: 0.8249 - categorical_accuracy: 0.6872

289/289 [==============================] - 28s 96ms/step - loss: 0.8249 - categorical_accuracy: 0.6872 - val_loss: 0.8243 - val_categorical_accuracy: 0.6794
Epoch 6/10

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289/289 [==============================] - 26s 89ms/step - loss: 0.7653 - categorical_accuracy: 0.7097 - val_loss: 0.6950 - val_categorical_accuracy: 0.7438
Epoch 7/10

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289/289 [==============================] - 26s 90ms/step - loss: 0.7286 - categorical_accuracy: 0.7269 - val_loss: 0.6843 - val_categorical_accuracy: 0.7340
Epoch 8/10

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289/289 [==============================] - 25s 85ms/step - loss: 0.6710 - categorical_accuracy: 0.7454

289/289 [==============================] - 26s 90ms/step - loss: 0.6710 - categorical_accuracy: 0.7454 - val_loss: 0.6922 - val_categorical_accuracy: 0.7432
Epoch 9/10

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289/289 [==============================] - 22s 77ms/step - loss: 0.6428 - categorical_accuracy: 0.7555

289/289 [==============================] - 24s 82ms/step - loss: 0.6428 - categorical_accuracy: 0.7555 - val_loss: 0.6293 - val_categorical_accuracy: 0.7584
Epoch 10/10

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289/289 [==============================] - 26s 90ms/step - loss: 0.6130 - categorical_accuracy: 0.7673 - val_loss: 0.5567 - val_categorical_accuracy: 0.7937
processing fold # 7 
Epoch 1/10

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289/289 [==============================] - 27s 93ms/step - loss: 1.6255 - categorical_accuracy: 0.3930 - val_loss: 1.3552 - val_categorical_accuracy: 0.4765
Epoch 2/10

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289/289 [==============================] - 25s 86ms/step - loss: 1.2190 - categorical_accuracy: 0.5372

289/289 [==============================] - 27s 92ms/step - loss: 1.2190 - categorical_accuracy: 0.5372 - val_loss: 1.0652 - val_categorical_accuracy: 0.6055
Epoch 3/10

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289/289 [==============================] - 25s 86ms/step - loss: 1.0288 - categorical_accuracy: 0.6101

289/289 [==============================] - 26s 91ms/step - loss: 1.0288 - categorical_accuracy: 0.6101 - val_loss: 0.9072 - val_categorical_accuracy: 0.6572
Epoch 4/10

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289/289 [==============================] - 26s 90ms/step - loss: 0.9094 - categorical_accuracy: 0.6528

289/289 [==============================] - 28s 96ms/step - loss: 0.9094 - categorical_accuracy: 0.6528 - val_loss: 0.8769 - val_categorical_accuracy: 0.6579
Epoch 5/10

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289/289 [==============================] - 24s 83ms/step - loss: 0.8259 - categorical_accuracy: 0.6839

289/289 [==============================] - 26s 89ms/step - loss: 0.8259 - categorical_accuracy: 0.6839 - val_loss: 0.8175 - val_categorical_accuracy: 0.6779
Epoch 6/10

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289/289 [==============================] - 27s 92ms/step - loss: 0.7922 - categorical_accuracy: 0.7012 - val_loss: 0.7331 - val_categorical_accuracy: 0.7192
Epoch 7/10

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289/289 [==============================] - 25s 86ms/step - loss: 0.7236 - categorical_accuracy: 0.7230

289/289 [==============================] - 27s 92ms/step - loss: 0.7236 - categorical_accuracy: 0.7230 - val_loss: 0.6586 - val_categorical_accuracy: 0.7486
Epoch 8/10

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289/289 [==============================] - 25s 87ms/step - loss: 0.6791 - categorical_accuracy: 0.7387

289/289 [==============================] - 27s 93ms/step - loss: 0.6791 - categorical_accuracy: 0.7387 - val_loss: 0.6670 - val_categorical_accuracy: 0.7457
Epoch 9/10

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289/289 [==============================] - 25s 86ms/step - loss: 0.6551 - categorical_accuracy: 0.7495

289/289 [==============================] - 26s 91ms/step - loss: 0.6551 - categorical_accuracy: 0.7495 - val_loss: 0.6145 - val_categorical_accuracy: 0.7608
Epoch 10/10

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289/289 [==============================] - 18s 62ms/step - loss: 0.6223 - categorical_accuracy: 0.7626

289/289 [==============================] - 18s 63ms/step - loss: 0.6223 - categorical_accuracy: 0.7626 - val_loss: 0.5882 - val_categorical_accuracy: 0.7755
processing fold # 8 
Epoch 1/10

  1/289 [..............................] - ETA: 1:38 - loss: 2.0846 - categorical_accuracy: 0.1230
 17/289 [>.............................] - ETA: 0s - loss: 2.0288 - categorical_accuracy: 0.2099  
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238/289 [=======================>......] - ETA: 0s - loss: 1.7164 - categorical_accuracy: 0.3626
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279/289 [===========================>..] - ETA: 0s - loss: 1.6786 - categorical_accuracy: 0.3762
289/289 [==============================] - 1s 3ms/step - loss: 1.6701 - categorical_accuracy: 0.3793

289/289 [==============================] - 2s 6ms/step - loss: 1.6701 - categorical_accuracy: 0.3793 - val_loss: 1.3360 - val_categorical_accuracy: 0.4979
Epoch 2/10

  1/289 [..............................] - ETA: 1s - loss: 1.3529 - categorical_accuracy: 0.4707
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273/289 [===========================>..] - ETA: 0s - loss: 1.2599 - categorical_accuracy: 0.5289
289/289 [==============================] - 1s 3ms/step - loss: 1.2529 - categorical_accuracy: 0.5316

289/289 [==============================] - 1s 4ms/step - loss: 1.2529 - categorical_accuracy: 0.5316 - val_loss: 1.0716 - val_categorical_accuracy: 0.6099
Epoch 3/10

  1/289 [..............................] - ETA: 1s - loss: 1.0865 - categorical_accuracy: 0.5859
 19/289 [>.............................] - ETA: 0s - loss: 1.1364 - categorical_accuracy: 0.5833
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158/289 [===============>..............] - ETA: 0s - loss: 1.0786 - categorical_accuracy: 0.5969
175/289 [=================>............] - ETA: 0s - loss: 1.0721 - categorical_accuracy: 0.5992
192/289 [==================>...........] - ETA: 0s - loss: 1.0686 - categorical_accuracy: 0.6007
207/289 [====================>.........] - ETA: 0s - loss: 1.0704 - categorical_accuracy: 0.6011
223/289 [======================>.......] - ETA: 0s - loss: 1.0635 - categorical_accuracy: 0.6040
240/289 [=======================>......] - ETA: 0s - loss: 1.0570 - categorical_accuracy: 0.6065
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276/289 [===========================>..] - ETA: 0s - loss: 1.0466 - categorical_accuracy: 0.6098
289/289 [==============================] - 1s 3ms/step - loss: 1.0423 - categorical_accuracy: 0.6113

289/289 [==============================] - 1s 4ms/step - loss: 1.0423 - categorical_accuracy: 0.6113 - val_loss: 1.0337 - val_categorical_accuracy: 0.6078
Epoch 4/10

  1/289 [..............................] - ETA: 0s - loss: 1.0810 - categorical_accuracy: 0.5918
 15/289 [>.............................] - ETA: 0s - loss: 0.9457 - categorical_accuracy: 0.6526
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286/289 [============================>.] - ETA: 0s - loss: 0.9076 - categorical_accuracy: 0.6593
289/289 [==============================] - 1s 3ms/step - loss: 0.9103 - categorical_accuracy: 0.6582

289/289 [==============================] - 1s 4ms/step - loss: 0.9103 - categorical_accuracy: 0.6582 - val_loss: 1.2359 - val_categorical_accuracy: 0.5475
Epoch 5/10

  1/289 [..............................] - ETA: 1s - loss: 1.2693 - categorical_accuracy: 0.5527
 18/289 [>.............................] - ETA: 0s - loss: 0.8893 - categorical_accuracy: 0.6707
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284/289 [============================>.] - ETA: 0s - loss: 0.8227 - categorical_accuracy: 0.6884
289/289 [==============================] - 1s 3ms/step - loss: 0.8214 - categorical_accuracy: 0.6889

289/289 [==============================] - 1s 4ms/step - loss: 0.8214 - categorical_accuracy: 0.6889 - val_loss: 0.7518 - val_categorical_accuracy: 0.7164
Epoch 6/10

  1/289 [..............................] - ETA: 0s - loss: 0.7355 - categorical_accuracy: 0.7246
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278/289 [===========================>..] - ETA: 0s - loss: 0.7710 - categorical_accuracy: 0.7099
289/289 [==============================] - 1s 3ms/step - loss: 0.7691 - categorical_accuracy: 0.7104

289/289 [==============================] - 1s 4ms/step - loss: 0.7691 - categorical_accuracy: 0.7104 - val_loss: 0.7158 - val_categorical_accuracy: 0.7291
Epoch 7/10

  1/289 [..............................] - ETA: 1s - loss: 0.7028 - categorical_accuracy: 0.7285
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105/289 [=========>....................] - ETA: 0s - loss: 0.7221 - categorical_accuracy: 0.7251
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284/289 [============================>.] - ETA: 0s - loss: 0.7084 - categorical_accuracy: 0.7300
289/289 [==============================] - 1s 3ms/step - loss: 0.7082 - categorical_accuracy: 0.7302

289/289 [==============================] - 1s 4ms/step - loss: 0.7082 - categorical_accuracy: 0.7302 - val_loss: 0.6764 - val_categorical_accuracy: 0.7449
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.6398 - categorical_accuracy: 0.7441
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289/289 [==============================] - 1s 3ms/step - loss: 0.6800 - categorical_accuracy: 0.7424

289/289 [==============================] - 1s 4ms/step - loss: 0.6800 - categorical_accuracy: 0.7424 - val_loss: 0.6488 - val_categorical_accuracy: 0.7614
Epoch 9/10

  1/289 [..............................] - ETA: 0s - loss: 0.6674 - categorical_accuracy: 0.7617
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289/289 [==============================] - 1s 3ms/step - loss: 0.6430 - categorical_accuracy: 0.7567

289/289 [==============================] - 1s 4ms/step - loss: 0.6430 - categorical_accuracy: 0.7567 - val_loss: 0.6102 - val_categorical_accuracy: 0.7707
Epoch 10/10

  1/289 [..............................] - ETA: 1s - loss: 0.5807 - categorical_accuracy: 0.8027
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283/289 [============================>.] - ETA: 0s - loss: 0.6207 - categorical_accuracy: 0.7666
289/289 [==============================] - 1s 3ms/step - loss: 0.6214 - categorical_accuracy: 0.7663

289/289 [==============================] - 1s 4ms/step - loss: 0.6214 - categorical_accuracy: 0.7663 - val_loss: 0.7330 - val_categorical_accuracy: 0.7334
processing fold # 9 
Epoch 1/10

  1/289 [..............................] - ETA: 1:09 - loss: 2.0892 - categorical_accuracy: 0.1387
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289/289 [==============================] - 1s 4ms/step - loss: 1.6744 - categorical_accuracy: 0.3754

289/289 [==============================] - 2s 7ms/step - loss: 1.6744 - categorical_accuracy: 0.3754 - val_loss: 1.3722 - val_categorical_accuracy: 0.4727
Epoch 2/10

  1/289 [..............................] - ETA: 1s - loss: 1.3322 - categorical_accuracy: 0.4863
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289/289 [==============================] - 2s 8ms/step - loss: 1.2240 - categorical_accuracy: 0.5396

289/289 [==============================] - 3s 9ms/step - loss: 1.2240 - categorical_accuracy: 0.5396 - val_loss: 1.1020 - val_categorical_accuracy: 0.5871
Epoch 3/10

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289/289 [==============================] - 3s 12ms/step - loss: 1.0307 - categorical_accuracy: 0.6127

289/289 [==============================] - 4s 14ms/step - loss: 1.0307 - categorical_accuracy: 0.6127 - val_loss: 1.1423 - val_categorical_accuracy: 0.5686
Epoch 4/10

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289/289 [==============================] - 5s 17ms/step - loss: 0.9097 - categorical_accuracy: 0.6574

289/289 [==============================] - 5s 18ms/step - loss: 0.9097 - categorical_accuracy: 0.6574 - val_loss: 0.9058 - val_categorical_accuracy: 0.6612
Epoch 5/10

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289/289 [==============================] - 1s 3ms/step - loss: 0.8311 - categorical_accuracy: 0.6840

289/289 [==============================] - 1s 4ms/step - loss: 0.8311 - categorical_accuracy: 0.6840 - val_loss: 0.7859 - val_categorical_accuracy: 0.7000
Epoch 6/10

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289/289 [==============================] - 1s 3ms/step - loss: 0.7736 - categorical_accuracy: 0.7071

289/289 [==============================] - 1s 4ms/step - loss: 0.7736 - categorical_accuracy: 0.7071 - val_loss: 0.7259 - val_categorical_accuracy: 0.7255
Epoch 7/10

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289/289 [==============================] - 1s 3ms/step - loss: 0.7188 - categorical_accuracy: 0.7268

289/289 [==============================] - 1s 4ms/step - loss: 0.7188 - categorical_accuracy: 0.7268 - val_loss: 0.6832 - val_categorical_accuracy: 0.7409
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.6609 - categorical_accuracy: 0.7324
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289/289 [==============================] - 1s 3ms/step - loss: 0.6876 - categorical_accuracy: 0.7383

289/289 [==============================] - 1s 4ms/step - loss: 0.6876 - categorical_accuracy: 0.7383 - val_loss: 0.6768 - val_categorical_accuracy: 0.7452
Epoch 9/10

  1/289 [..............................] - ETA: 0s - loss: 0.6393 - categorical_accuracy: 0.7754
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289/289 [==============================] - 1s 3ms/step - loss: 0.6574 - categorical_accuracy: 0.7494

289/289 [==============================] - 1s 4ms/step - loss: 0.6574 - categorical_accuracy: 0.7494 - val_loss: 0.6425 - val_categorical_accuracy: 0.7558
Epoch 10/10

  1/289 [..............................] - ETA: 0s - loss: 0.5841 - categorical_accuracy: 0.7910
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289/289 [==============================] - 1s 3ms/step - loss: 0.6266 - categorical_accuracy: 0.7611

289/289 [==============================] - 1s 3ms/step - loss: 0.6266 - categorical_accuracy: 0.7611 - val_loss: 0.5800 - val_categorical_accuracy: 0.7837
processing fold # 10 
Epoch 1/10

  1/289 [..............................] - ETA: 1:12 - loss: 2.0960 - categorical_accuracy: 0.1191
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289/289 [==============================] - 1s 3ms/step - loss: 1.6917 - categorical_accuracy: 0.3683

289/289 [==============================] - 2s 6ms/step - loss: 1.6917 - categorical_accuracy: 0.3683 - val_loss: 1.3520 - val_categorical_accuracy: 0.4928
Epoch 2/10

  1/289 [..............................] - ETA: 0s - loss: 1.3952 - categorical_accuracy: 0.4824
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288/289 [============================>.] - ETA: 0s - loss: 1.2671 - categorical_accuracy: 0.5208
289/289 [==============================] - 1s 3ms/step - loss: 1.2668 - categorical_accuracy: 0.5209

289/289 [==============================] - 1s 4ms/step - loss: 1.2668 - categorical_accuracy: 0.5209 - val_loss: 1.1595 - val_categorical_accuracy: 0.5554
Epoch 3/10

  1/289 [..............................] - ETA: 0s - loss: 1.1847 - categorical_accuracy: 0.5332
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289/289 [==============================] - 1s 3ms/step - loss: 1.0714 - categorical_accuracy: 0.5956

289/289 [==============================] - 1s 4ms/step - loss: 1.0714 - categorical_accuracy: 0.5956 - val_loss: 0.9670 - val_categorical_accuracy: 0.6421
Epoch 4/10

  1/289 [..............................] - ETA: 0s - loss: 0.9906 - categorical_accuracy: 0.6230
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289/289 [==============================] - 1s 3ms/step - loss: 0.9316 - categorical_accuracy: 0.6473

289/289 [==============================] - 1s 3ms/step - loss: 0.9316 - categorical_accuracy: 0.6473 - val_loss: 0.9700 - val_categorical_accuracy: 0.6217
Epoch 5/10

  1/289 [..............................] - ETA: 0s - loss: 0.8949 - categorical_accuracy: 0.6602
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289/289 [==============================] - 1s 3ms/step - loss: 0.8485 - categorical_accuracy: 0.6783

289/289 [==============================] - 1s 3ms/step - loss: 0.8485 - categorical_accuracy: 0.6783 - val_loss: 0.8343 - val_categorical_accuracy: 0.6744
Epoch 6/10

  1/289 [..............................] - ETA: 0s - loss: 0.8029 - categorical_accuracy: 0.6836
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289/289 [==============================] - 1s 3ms/step - loss: 0.8065 - categorical_accuracy: 0.6960

289/289 [==============================] - 1s 3ms/step - loss: 0.8065 - categorical_accuracy: 0.6960 - val_loss: 0.7547 - val_categorical_accuracy: 0.7130
Epoch 7/10

  1/289 [..............................] - ETA: 0s - loss: 0.7445 - categorical_accuracy: 0.7461
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289/289 [==============================] - 1s 3ms/step - loss: 0.7405 - categorical_accuracy: 0.7185

289/289 [==============================] - 1s 4ms/step - loss: 0.7405 - categorical_accuracy: 0.7185 - val_loss: 0.8469 - val_categorical_accuracy: 0.6683
Epoch 8/10

  1/289 [..............................] - ETA: 0s - loss: 0.8462 - categorical_accuracy: 0.6777
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289/289 [==============================] - 1s 3ms/step - loss: 0.6968 - categorical_accuracy: 0.7352

289/289 [==============================] - 1s 4ms/step - loss: 0.6968 - categorical_accuracy: 0.7352 - val_loss: 0.7027 - val_categorical_accuracy: 0.7298
Epoch 9/10

  1/289 [..............................] - ETA: 0s - loss: 0.7354 - categorical_accuracy: 0.7207
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271/289 [===========================>..] - ETA: 0s - loss: 0.6694 - categorical_accuracy: 0.7469
289/289 [==============================] - 1s 3ms/step - loss: 0.6671 - categorical_accuracy: 0.7479

289/289 [==============================] - 1s 3ms/step - loss: 0.6671 - categorical_accuracy: 0.7479 - val_loss: 0.6435 - val_categorical_accuracy: 0.7533
Epoch 10/10

  1/289 [..............................] - ETA: 0s - loss: 0.6548 - categorical_accuracy: 0.7637
 20/289 [=>............................] - ETA: 0s - loss: 0.6653 - categorical_accuracy: 0.7443
 39/289 [===>..........................] - ETA: 0s - loss: 0.6461 - categorical_accuracy: 0.7518
 57/289 [====>.........................] - ETA: 0s - loss: 0.6485 - categorical_accuracy: 0.7504
 75/289 [======>.......................] - ETA: 0s - loss: 0.6480 - categorical_accuracy: 0.7512
 94/289 [========>.....................] - ETA: 0s - loss: 0.6491 - categorical_accuracy: 0.7511
115/289 [==========>...................] - ETA: 0s - loss: 0.6527 - categorical_accuracy: 0.7497
136/289 [=============>................] - ETA: 0s - loss: 0.6469 - categorical_accuracy: 0.7528
155/289 [===============>..............] - ETA: 0s - loss: 0.6451 - categorical_accuracy: 0.7531
174/289 [=================>............] - ETA: 0s - loss: 0.6510 - categorical_accuracy: 0.7520
192/289 [==================>...........] - ETA: 0s - loss: 0.6484 - categorical_accuracy: 0.7531
210/289 [====================>.........] - ETA: 0s - loss: 0.6492 - categorical_accuracy: 0.7528
230/289 [======================>.......] - ETA: 0s - loss: 0.6463 - categorical_accuracy: 0.7541
249/289 [========================>.....] - ETA: 0s - loss: 0.6440 - categorical_accuracy: 0.7550
268/289 [==========================>...] - ETA: 0s - loss: 0.6422 - categorical_accuracy: 0.7557
286/289 [============================>.] - ETA: 0s - loss: 0.6394 - categorical_accuracy: 0.7568
289/289 [==============================] - 1s 3ms/step - loss: 0.6391 - categorical_accuracy: 0.7568

289/289 [==============================] - 1s 3ms/step - loss: 0.6391 - categorical_accuracy: 0.7568 - val_loss: 0.6441 - val_categorical_accuracy: 0.7500

#reticulate::py_last_error()

#We can then compute the average of the per-epoch ACC scores for all folds:

eval

                loss categorical_accuracy 
           0.8336558            0.7033769 

# all_scores
# mean(all_scores)

  ## Take Model1 and Scale data
  ##scale and preprocess training and test data
  #library(caret)
  pre_proc_val <- preProcess(train, method = c("center", "scale"))  
  train = predict(pre_proc_val, train)
  test = predict(pre_proc_val, test)
  train <- tensor_slices_dataset(train)
  test <- tensor_slices_dataset(test)
  summary(train)
  

str(train_data[[1]])